The AI PM Playbook: Finding Product-Market Fit Faster
About this session
Everyone can build an AI prototype today—but very few teams know when they have built a product customers truly value.
In this lightning talk, I'll share a practical framework for identifying AI product-market fit using real customer signals instead of vanity metrics. Drawing from my experience building AI-powered experiences for Microsoft 365 Copilot, we'll explore how to distinguish curiosity from sustained adoption, what metrics actually predict long-term success, and the warning signs that indicate it's time to pivot.
Attendees will leave with actionable frameworks for evaluating AI products across customer value, trust, engagement, and business impact, along with practical questions every AI product team should ask before investing further. Whether you're building copilots, AI agents, or enterprise AI applications, this session will help you move beyond impressive demos and build products users consistently return to.
Speaker
Key takeaways
- Recognize the signals of AI product-market fit by distinguishing meaningful customer value from vanity metrics like prompt volume or demo engagement.
- Measure what matters using a practical framework that evaluates user adoption, trust, retention, and business impact—not just model performance.
- Know when to pivot, persevere, or scale by identifying early indicators that an AI product needs iteration before further investment